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* refactor: move legacy code to archive/ directory - Moved ktransformers, csrc, third_party, merge_tensors to archive/ - Moved build scripts and configurations to archive/ - Kept kt-kernel, KT-SFT, doc, and README files in root - Preserved complete git history for all moved files * refactor: restructure repository to focus on kt-kernel and KT-SFT modules * fix README * fix README * fix README * fix README * docs: add performance benchmarks to kt-kernel section Add comprehensive performance data for kt-kernel to match KT-SFT's presentation: - AMX kernel optimization: 21.3 TFLOPS (3.9× faster than PyTorch) - Prefill phase: up to 20× speedup vs baseline - Decode phase: up to 4× speedup - NUMA optimization: up to 63% throughput improvement - Multi-GPU (8×L20): 227.85 tokens/s total throughput with DeepSeek-R1 FP8 Source: https://lmsys.org/blog/2025-10-22-KTransformers/ This provides users with concrete performance metrics for both core modules, making it easier to understand the capabilities of each component. * refactor: improve kt-kernel performance data with specific hardware and models Replace generic performance descriptions with concrete benchmarks: - Specify exact hardware: 8×L20 GPU + Xeon Gold 6454S, Single/Dual-socket Xeon + AMX - Include specific models: DeepSeek-R1-0528 (FP8), DeepSeek-V3 (671B) - Show detailed metrics: total throughput, output throughput, concurrency details - Match KT-SFT presentation style for consistency This provides users with actionable performance data they can use to evaluate hardware requirements and expected performance for their use cases. * fix README * docs: clean up performance table and improve formatting * add pic for README * refactor: simplify .gitmodules and backup legacy submodules - Remove 7 legacy submodules from root .gitmodules (archive/third_party/*) - Keep only 2 active submodules for kt-kernel (llama.cpp, pybind11) - Backup complete .gitmodules to archive/.gitmodules - Add documentation in archive/README.md for researchers who need legacy submodules This reduces initial clone size by ~500MB and avoids downloading unused dependencies. * refactor: move doc/ back to root directory Keep documentation in root for easier access and maintenance. * refactor: consolidate all images to doc/assets/ - Move kt-kernel/assets/heterogeneous_computing.png to doc/assets/ - Remove KT-SFT/assets/ (images already in doc/assets/) - Update KT-SFT/README.md image references to ../doc/assets/ - Eliminates ~7.9MB image duplication - Centralizes all documentation assets in one location * fix pic path for README
125 lines
3.8 KiB
C++
125 lines
3.8 KiB
C++
/**
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* @Description :
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* @Author : Xie Weiyu
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* @Date : 2024-11-22 08:29:45
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* @Version : 1.0.0
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* @LastEditors : Xie Weiyu
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* @LastEditTime : 2024-11-22 09:56:12
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* @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
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**/
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#include <future>
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#include "common.hpp"
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int main(int argc, char* argv[]) {
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init(argc, argv);
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spdlog::set_level(spdlog::level::trace);
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auto kvc2 = kvc2::create_kvc2(config);
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std::mt19937 gen(123);
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std::vector<std::vector<Token>> ids;
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std::vector<std::vector<layer_data>> k, v;
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for (size_t i = 0; i < 10; i++) {
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ids.push_back(random_ids(1 * config.num_token_per_page, gen));
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k.push_back(random_kvcache(1, gen));
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v.push_back(random_kvcache(1, gen));
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kvc2->raw_insert(test_model_name, test_quant_type, ids[i].data(), ids[i].size(), k[i], v[i]);
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}
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kvc2->debug();
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{
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// all match
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std::vector<Token*> chunks;
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std::vector<TokenLength> lengths;
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for (size_t i = 0; i < 10; i++) {
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chunks.push_back(ids[i].data());
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lengths.push_back(ids[i].size());
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}
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std::promise<std::shared_ptr<DoubleCacheHandleInterface>> p;
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kvc2->lookup_alt_to_gpu_async(test_model_name, test_quant_type, chunks, lengths, 15 * config.num_token_per_page,
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[&p](std::shared_ptr<DoubleCacheHandleInterface> h) { p.set_value(h); });
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auto fut = p.get_future();
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fut.wait();
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auto h = fut.get();
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auto hk = h->handle_data(true);
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auto hv = h->handle_data(false);
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for (size_t i = 0; i < 10; i++) {
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cmp_handle_data(slice(hk, i, i + 1), k[i], 1);
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cmp_handle_data(slice(hv, i, i + 1), v[i], 1);
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}
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auto block_idx = h->get_gpu_block_idx();
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auto [kcache, vcache] = kvc2->get_kvcache();
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for (size_t i = 0; i < 10; i++) {
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std::vector<size_t> blocks = {block_idx[i]};
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cmp_handle_gpu(blocks, kcache, vcache, k[i], v[i], 1);
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}
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}
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{
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// no match in the middle
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std::vector<Token*> chunks;
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std::vector<TokenLength> lengths;
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std::vector<std::vector<Token>> new_ids;
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for (size_t i = 0; i < 10; i++) {
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new_ids.push_back(random_ids(1 * config.num_token_per_page, gen));
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}
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for (size_t i = 0; i < 10; i++) {
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if (i == 1 || i == 5 || i == 6) {
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chunks.push_back(new_ids[i].data());
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} else {
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chunks.push_back(ids[i].data());
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}
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lengths.push_back(ids[i].size());
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}
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std::promise<std::shared_ptr<DoubleCacheHandleInterface>> p;
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kvc2->lookup_alt_to_gpu_async(test_model_name, test_quant_type, chunks, lengths, 15 * config.num_token_per_page,
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[&p](std::shared_ptr<DoubleCacheHandleInterface> h) { p.set_value(h); });
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auto fut = p.get_future();
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fut.wait();
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auto h = fut.get();
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auto statuses = h->matched_status();
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for (size_t i = 0; i < 10; i++) {
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if (i == 1) {
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assert(statuses[i] == MatchStatus::NotMatchExact);
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} else if (i == 5 || i == 6) {
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assert(statuses[i] == MatchStatus::NotMatchPartial);
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} else if (i == 0) {
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assert(statuses[i] == MatchStatus::Exact);
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} else {
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assert(statuses[i] == MatchStatus::Partial);
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}
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}
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auto hk = h->handle_data(true);
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auto hv = h->handle_data(false);
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for (size_t i = 0; i < 10; i++) {
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if (i == 1 || i == 5 || i == 6) {
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} else {
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cmp_handle_data(slice(hk, i, i + 1), k[i], 1);
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cmp_handle_data(slice(hv, i, i + 1), v[i], 1);
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}
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}
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auto block_idx = h->get_gpu_block_idx();
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auto [kcache, vcache] = kvc2->get_kvcache();
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for (size_t i = 0; i < 10; i++) {
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if (i == 1 || i == 5 || i == 6) {
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} else {
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std::vector<size_t> blocks = {block_idx[i]};
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cmp_handle_gpu(blocks, kcache, vcache, k[i], v[i], 1);
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}
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}
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}
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SPDLOG_CRITICAL("All Test Passed: {}", argv[0]);
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return 0;
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}
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